Publication Rate of Abstracts Presented at the North American Neuro-Ophthalmology Society Annual Meeting From 2008 to 2017
Bibliographic record
Abstract
BACKGROUND: Conference abstracts serve an important role in the timely dissemination of scientific and clinical advancements, but most fail to be published. The goal of this study was to investigate the publication rate and factors associated with publication of abstracts presented at the North American Neuro-Ophthalmology Society (NANOS) Annual Meeting over a 10-year period. METHODS: NANOS Annual Meeting abstracts from 2008 to 2017 were extracted and categorized into Walsh presentations, scientific platforms, or poster presentations. An original automated web scraping program was validated to search PubMed, Embase, Medline, and Google Scholar for publications. Publication date, journal, authors, study type, multicenter involvement, and financial disclosures were retrieved. RESULTS: A total of 195 Walsh presentations, 231 scientific platform presentations, and 1735 scientific posters were included in the study with an overall publication rate of 31.5% (681/2,161). This was stable over the study period. Publication was the highest for scientific platforms (67.1%), followed by Walsh abstracts (36.4%) and poster presentations (27.2%). Multivariable analysis identified 3-4 authors, 5 or more authors, basic science, and sample size of 100 or more significantly correlated with subsequent publication. The top 3 countries for NANOS submissions were the United States, Canada, and South Korea, and the most frequent journal of publication was the Journal of Neuro-Ophthalmology. CONCLUSIONS: Publication rate of NANOS abstracts is comparable to other conferences in ophthalmology and the neurological sciences. Conference attendees should be aware that more than two-thirds of abstracts fail to be published and publication rates vary widely by type of submission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".